Abstract: Features are extracted and tabulated for transactions on a per-operator basis. Metrics are derived from the features on a per-feature basis and on an aggregate bases for the features as a whole. Additional metrics are calculated on a per-feature basis and an aggregate bases for a group of operators. A current transaction being conducted in real time by a given operator has current features extracted and a risk score is generated. The risk score is compared against the metrics associated with the given operator and the group of operators. A determination is made as to whether the current transaction is fraud or is not fraud based on the comparison. When fraud is detected, an alert is raised, and the current transaction provided to a fraud system for further fraud evaluation.

Abstract: An initial small group of customers are randomly selected for participation in a campaign associated with a promotion. Transactions of the group are monitored for a redemption of the promotion. A redeeming member's transaction is modeled and mapped into a multidimensional space and transactions for customers not included in the group are modeled and mapped into the space. A neighborhood of the redeeming member and select ones of the customers not included in the group are detected as emerging from the space. Most similar customers to the redeeming member are determined from the neighborhood and a preconfigured number of the most similar customers are selected for inclusion within the group for the campaign. This process continues until a goal of the campaign is reached or the campaign is ended.

Abstract: An initial small group of customers are randomly selected for participation in a campaign associated with a promotion. Transactions of the group are monitored for a redemption of the promotion. A redeeming member's transaction is modeled and mapped into a multidimensional space and transactions for customers not included in the group are modeled and mapped into the space. A neighborhood of the redeeming member and select ones of the customers not included in the group are detected as emerging from the space. Most similar customers to the redeeming member are determined from the neighborhood and a preconfigured number of the most similar customers are selected for inclusion within the group for the campaign. This process continues until a goal of the campaign is reached or the campaign is ended.

Abstract: Features are extracted and tabulated for transactions on a per-operator basis. Metrics are derived from the features on a per-feature basis and on an aggregate bases for the features as a whole. Additional metrics are calculated on a per-feature basis and an aggregate bases for a group of operators. A current transaction being conducted in real time by a given operator has current features extracted and a risk score is generated. The risk score is compared against the metrics associated with the given operator and the group of operators. A determination is made as to whether the current transaction is fraud or is not fraud based on the comparison. When fraud is detected, an alert is raised, and the current transaction provided to a fraud system for further fraud evaluation.

Abstract: Data is collected for Self-Service Terminals (SSTs) including tallies, events, and outcomes associated with servicing the SSTs. Statistical correlations are derived from the tallies and events with respect to the outcomes. Subsequent collected data is processed with the statistical correlations and a probability for a failure of a component or a part of the component associated with a particular SST is reported for servicing the component or part before the failure.

Abstract: Various embodiments herein include an engine that uses data science, history, and trends to successfully determine and suggest the complimentary add on and upsell items that are tailored to at least some of the customer, current trends, product popularity, recommendations, and other such factors. One embodiment, in the form of a method, includes receiving, from a process involved in processing an open transaction, data representative of at least one of products and services that are subjects of the open transaction. The method further includes querying a recommendation engine for at least one of product and service recommendations based on the received data representative of products and services and receiving, in response to the query, at least one product or service recommendation. The method may then provide the at least one product recommendation to the process involved in processing the open transaction.

Abstract: Security rules of a security system include original security scores as conditions for raising alerts. The original security scores are dependent on the underlying scoring mechanism of the security system. When the scoring mechanism is updated to produce new security scores, the security rules are updated ensuring that the alert rates associated with the original security scores match the alert rates associated with the new security scores, which replace the original security scores in the security rules.

Abstract: Data is collected for Self-Service Terminals (SSTs) including tallies, events, and outcomes associated with servicing the SSTs. Statistical correlations are derived from the tallies and events with respect to the outcomes. Subsequent collected data is processed with the statistical correlations and a probability for a failure of a component or a part of the component associated with a particular SST is reported for servicing the component or part before the failure.

Abstract: Various embodiments herein include an engine that uses data science, history, and trends to successfully determine and suggest the complimentary add on and upsell items that are tailored to at least some of the customer, current trends, product popularity, recommendations, and other such factors. One embodiment, in the form of a method, includes receiving, from a process involved in processing an open transaction, data representative of at least one of products and services that are subjects of the open transaction. The method further includes querying a recommendation engine for at least one of product and service recommendations based on the received data representative of products and services and receiving, in response to the query, at least one product or service recommendation. The method may then provide the at least one product recommendation to the process involved in processing the open transaction.

Abstract: Security rules of a security system include original security scores as conditions for raising alerts. The original security scores are dependent on the underlying scoring mechanism of the security system. When the scoring mechanism is updated to produce new security scores, the security rules are updated ensuring that the alert rates associated with the original security scores match the alert rates associated with the new security scores, which replace the original security scores in the security rules.

Abstract: An educational recommender system and a method are provided. The method includes receiving a request to recommend a course of action related to a plurality of current students; accessing a computer database storing student data that corresponds to the plurality of current students; clustering in a computer process the plurality of current students into at least two clusters based at least on granular assessment data associated with student data corresponding to respective current students; and outputting the results of the clustering to a user. The granular assessment data includes a result of an assessment administered to respective students of the plurality of current students, and each assessment includes a plurality of questions for assessing one of the current students. The associated result includes an independent evaluation of each respective question of the plurality of questions.

Abstract: A method is employed to present image from an event. A plurality of images from the event are received and one or more clusters of images are created, wherein each cluster of images has a similarity greater than a predetermined threshold. A density value of the data distribution within each cluster is estimated and at least one local maximum associated with each cluster is identified via a density function. At least one image from each cluster is selected, wherein each image is a candidate for the presentation. A layout is created to present the images selected.

Abstract: An automated system and a method for extracting a region of interest from a digital image are disclosed. The method includes identifying a subset of training images from a larger set of training images, each training image in the set having a respective identified region of interest. The subset is identified based on a measure of similarity between the digital image and the images in the set of training images. At least one region of interest is extracted from the digital image based on an analysis of the identified regions of interest in the subset of training images.

Abstract: A recommender system and method including receiving a request identifying at least one current student for which a recommendation is sought and accessing stored student data including attributes associated with respective students of the current students and a plurality of predecessor students. For the predecessor students the attributes include educational resources. The method includes clustering one of predecessor students and the current students into clusters based on sameness of first selected attributes of their respective associated attributes. The method includes generating a mapping between respective students of the other of the predecessor and current students and one of the clusters based on sameness of second selected attributes associated with the students being mapped. The method then includes recommending for each of the current students the educational resource associated with the predecessor student or cluster of predecessor students that the current student is mapped with.

Abstract: As set forth herein, a computer-based method is employed to align a sequences of images. Metadata associated with images from two or more sources is received and a time stamp is extracted from the metadata. The images are sorted into sequences based at least in part upon the image source. The similarity of images from disparate sequences is measured and image pairs from disparate sequences with a similarity greater than a predetermined threshold are identified. A sequence of images is aligned by minimizing the misalignment of pairs.

Abstract: An apparatus and method for detecting a region of interest in an image are disclosed. Image representations for a set of images that have been manually annotated with regions of interest are stored, along with positive and negative representations of each image which are similarly derived to the image representations except that they are based on features extracted from patches within the region of interest and outside it, respectively. For an original image for which a region of interest is desired, the stored information for K similar images is automatically retrieved and used to train a classifier. The trained classifier provides, for each patch of the original image, a probability of being in a region of interest, based extracted features of the patch (represented, for example, as a Fisher vector), which can be used to determine a region of interest in the original image.

Abstract: A method is employed to present image from an event. A plurality of images from the event are received and one or more clusters of images are created, wherein each cluster of images has a similarity greater than a predetermined threshold. A density value of the data distribution within each cluster is estimated and at least one local maximum associated with each cluster is identified via a density function. At least one image from each cluster is selected, wherein each image is a candidate for the presentation. A layout is created to present the images selected.

Abstract: An apparatus and method for detecting a region of interest in an image are disclosed. Image representations for a set of images that have been manually annotated with regions of interest are stored, along with positive and negative representations of each image which are similarly derived to the image representations except that they are based on features extracted from patches within the region of interest and outside it, respectively. For an original image for which a region of interest is desired, the stored information for K similar images is automatically retrieved and used to train a classifier. The trained classifier provides, for each patch of the original image, a probability of being in a region of interest, based extracted features of the patch (represented, for example, as a Fisher vector), which can be used to determine a region of interest in the original image.

Abstract: A recommender system and method is provided, including receiving a request to recommend a course of action related to a plurality of current students in accordance with a plurality of constraints and accessing a computer database storing student data that corresponds to the plurality of current students. The student data includes attribute data corresponding to respective students of the plurality of current students for describing at least one attribute related to the respective students. The method further includes clustering in a computer process the plurality of current students into a selected number of clusters based at least on sameness of attribute data corresponding to the respective current students of the plurality of current students and the plurality of constraints, and outputting the results of the clustering to a user.

Abstract: A recommender system and method including receiving a request identifying at least one current student for which a recommendation is sought and accessing stored student data including attributes associated with respective students of the current students and a plurality of predecessor students. For the predecessor students the attributes include educational resources. The method includes clustering one of predecessor students and the current students into clusters based on sameness of first selected attributes of their respective associated attributes. The method includes generating a mapping between respective students of the other of the predecessor and current students and one of the clusters based on sameness of second selected attributes associated with the students being mapped. The method then includes recommending for each of the current students the educational resource associated with the predecessor student or cluster of predecessor students that the current student is mapped with.